Investors express their views about stocks by collectively holding the market portfolio, by doing so market capitalizations represent investors’ trading choices to hold securities according to their preferences in terms of expected risk and return. If investors seek to maximize their utility in holding portfolios and do hold their desired and optimal portfolio, then market information – contained in prices – contains investors’ expected returns on individual securities.
The logic that applies to the previous statements can be found in any investment management textbook. Market commentators, newspapers, speculators, and investors know market prices reflect – to a certain extent – available information on securities and thus incorporate investors’ expected returns.
Hi, I was wondering if you would make available the R code you used for the graphics, or is that proprietary? Also, is the mkt implied return coming from the Black-Litterman model?
Thank you – these are great visualization!
Hi Matt, sorry for this delay but I think I’m just getting kind of used to getting spam comments instead of genuine remarks and fail to check the comment area on the dashboard. The R code I used is attached at the end of the (draft) paper, just copy and paste on your R console. I haven’t updated it since because something’s gotten changed on iShares’ website and the getHoldings() function of the qmao package throws an exception indicating no data found. I’ll soon (hopefully) have to come up with my own function to web scrape using R the updated ETF holding weights.
As with your question the BL approach “implies” at the outset an extraction of market implied expected returns -here dubbed with a catchy call sign called MICERs. I think if you’re a market commentator you can potentially extract valuable information from these visualizations, as long as you can rely on a reliable data source!